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Community Blog A Guide to Deploy QWEN3 Inference in Alibaba Cloud PAI's Model Gallery

A Guide to Deploy QWEN3 Inference in Alibaba Cloud PAI's Model Gallery

Alibaba Cloud PAI with EAS and Model Gallery enables one‑click AI deployment, stress testing, and monitoring for secure, scalable enterprise adoption.

Executive Summary

Alibaba Cloud’s Platform for Artificial Intelligence (PAI) is a comprehensive, enterprise-grade machine learning platform designed to streamline the entire AI lifecycle—from data preparation and model training to deployment and monitoring. At the core of PAI’s deployment capabilities is Elastic Algorithm Service (PAI-EAS), a fully managed, scalable inference service that enables organizations to rapidly deploy machine learning models as high-performance, production-ready APIs.

A key innovation that accelerates time-to-value is PAI Model Gallery, a curated repository of pre-optimized models from Alibaba’s internal research (e.g., Qwen series), Hugging Face, ModelScope, and the open-source community. Model Gallery integrates seamlessly with PAI-EAS to deliver one-click deployment for hundreds of models, eliminating the need for containerization, dependency management, or infrastructure configuration.

With PAI-EAS and Model Gallery, users can:

  1. Deploy in minutes: Select a model from the gallery and deploy it with a single click - no coding or DevOps expertise required.
  2. Ensure performance and cost-efficiency: Automatically leverage optimized inference engines (including vLLM for large language models), GPU/TPU acceleration, and auto-scaling.
  3. Support diverse frameworks: Deploy models built with PyTorch, TensorFlow, ONNX, and more, with built-in support for popular architectures like Llama, Qwen, and Stable Diffusion.
  4. Maintain governance and security: Benefit from Alibaba Cloud’s enterprise-grade security, VPC isolation, and monitoring tools.

This integration dramatically lowers the barrier to AI adoption, enabling data scientists and developers to focus on innovation rather than infrastructure. For enterprises scaling generative AI, computer vision, or NLP applications, PAI-EAS with Model Gallery provides a secure, efficient, and user-friendly path from model to production - accelerating ROI and driving intelligent transformation across the organization.

Step-by-Step Guide: Deploying Qwen3 from PAI Model Gallery
This guide assumes your Alibaba Cloud PAI environment is already set up and that you have an active workspace. If you haven’t configured your environment yet, please refer to the official PAI setup guides before proceeding here.

Deploying a Large Language Model (LLM) for inference via the Model Gallery in PAI is fast, simple, and requires just a few steps.

PAI Workspace
To begin, access your workspace:

  1. Go to the PAI Console
  2. In the left navigation pane, click Workspaces.
  3. Select and enter your target workspace.

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PAI Model Gallery
PAI Model Gallery is a curated repository of pre-trained models from Alibaba’s ModelScope, Hugging Face, and the open-source community - optimized for seamless deployment on Alibaba Cloud PAI. It enables one-click inference setup for hundreds of models, including large language models like Qwen3, without writing code or managing infrastructure.

To deploy a model:

  1. In the left navigation pane, click Model Gallery.
  2. Search for your desired model (e.g., “Qwen3-1.7B-Base”) and click Deploy.

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PAI-EAS Deployment
A deployment configuration window will appear. Complete the following steps:

  1. Select an Inference Engine (e.g., vLLM for high-performance LLM serving)
  2. Choose a Deployment Template
  3. Choose Deployment Resources

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● Choose a VPC
● Choose a vSwitch
● Choose a Security Group

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Once you click Deploy, PAI-EAS automatically orchestrates the entire inference setup process. It provisions the selected compute instances, pulls the optimized model container (pre-configured with the chosen inference engine such as vLLM), mounts the model artifacts, and initializes the serving runtime. This end-to-end automation typically completes within a few minutes, after which the model is ready to serve low-latency predictions via API - without requiring any manual infrastructure management or container configuration.

PAI-EAS Inference Service
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PAI-EAS One-Click Stress Testing
PAI-EAS One-Click Stress Testing is a built-in feature in Alibaba Cloud’s Platform for AI – Elastic Algorithm Service (PAI-EAS) that allows users to quickly evaluate the performance, stability, and scalability of a deployed model service under simulated load - without writing any test scripts or setting up external tools.

With a single click in the PAI-EAS console, the system automatically:

  1. Generates concurrent inference requests to your deployed endpoint
  2. Simulates real-world traffic patterns (configurable QPS, concurrency, duration)

  3. Measures key metrics such as:

    ○ Latency (P50, P95, P99)
○ Requests Per Second (RPS/QPS)
○ Success/Error rates
○ GPU/CPU utilization
  4. Displays results in an intuitive dashboard

PAI-EAS Stress Test Task
To initaite a stress test on deployed service

  1. In the left navigation pane, click Elastic Algorithm Services
  2. Select Deployed Name from Inference Service tab
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  1. Select One-Click Stress Testing tab
  2. Click Create Stress Testing Task button

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  1. Configure test parameters as required

    ○ Enable LLM Service
○ Indicate Model ID
○ Enable Continuous Test
○ Specify Max Duration(s) in seconds
  2. Click Confirm to initiate test

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Once the task initialized, the stress test will commence. This automated testing process is designed to evaluate system performance under load, ensuring scalability, reliability, and optimal response times before production deployment.

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The system provides comprehensive real-time visibility into performance indicators.

  • Real-Time Monitoring Dashboard:
  1. Response Time (ms): Monitors average latency.
  2. Status Codes: Predominantly HTTP 200 responses, confirming successful request handling.
  3. QPS (Queries Per Second): Monitors steady throughput under load.
  4. Data Transfer (KB): Monitors both incoming and outgoing data.

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This stress test enables teams to:

  1. Identify performance thresholds and upper load limits.
  2. Validate auto-scaling behavior and resource utilization.
  3. Detect anomalies in latency or error rates early.
  4. Ensure high availability and robustness of AI services in production environments.

PAI-EAS Real-time Monitoring

The monitoring metrics in Alibaba Cloud’s Elastic Algorithm Service (EAS) provide comprehensive real-time insights into the performance, efficiency, and scalability of a deployed LLM service. They enable teams to track key aspects such as request throughput, latency (including end-to-end, time to first token, and per-token generation), inference timing, and resource utilization across GPU and CPU. By analyzing these metrics you can identify bottlenecks, optimize model performance, ensure stable response times, and improve cost-efficiency. These insights are critical for maintaining high availability, debugging issues, and validating system behavior under load - ensuring reliable and scalable AI service delivery.

PAI-EAS Monitoring (LLM)
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PAI-EAS Monitoring (Deployed Service)

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Summary: Deploying AI with Alibaba Cloud PAI

Alibaba Cloud’s Platform for Artificial Intelligence (PAI) provides an enterprise‑grade environment to manage the full AI lifecycle—from data preparation and training to deployment and monitoring.

At the heart of deployment is PAI‑EAS (Elastic Algorithm Service), a fully managed inference service that allows organizations to turn models into production‑ready APIs within minutes.

A major accelerator is the PAI Model Gallery, a curated repository of pre‑optimized models (including Alibaba’s Qwen series, Hugging Face, ModelScope, and open‑source). With one‑click integration into PAI‑EAS, it eliminates the need for containerization, dependency management, or infrastructure setup.

Benefits

  1. Rapid Deployment: One‑click model serving, no DevOps required.
  2. Optimized Performance: vLLM engine, GPU/TPU acceleration, auto‑scaling.
  3. Framework Flexibility: Supports PyTorch, TensorFlow, ONNX, and popular architectures (Qwen, Llama, Stable Diffusion).
  4. Enterprise Security: VPC isolation, monitoring, and governance built in.

Enterprise Impact

PAI‑EAS with Model Gallery lowers barriers to AI adoption, accelerates ROI, and enables enterprises to scale generative AI, computer vision, and NLP applications securely and efficiently—turning AI deployment into a one‑click innovation engine.

AI Trends Artificial Intelligence Deep Learning PAI artificial_intelligence Generative AI GenAI
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